Cognitive learning to counter security threats for kinematic actions in robots

ABSTRACT

A security control system for a kinematic robot uses a cognitive assessment agent to map proposed instructions to either legitimate or illegitimate actions based on contextual variables. The agent computes a security anomaly index score representing a variance of a likely kinematic action of the robot compared to acceptable actions. If the score exceeds a predetermined threshold, a security alert is generated for the robot&#39;s administrator. The contextual variables include a user profile, a user location, and subject matter of the kinematic actions. The analysis compares input text to predefined classification metadata, and can also compare verbal phrases or body gestures to corresponding baselines. Different numeric weights can be applied to the contextual variables. The computing begins with a default value for the score and thereafter increments or decrements the score based on the weights. The weights can be adjusted based on a supervisory appraisal of the computed score.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of copending U.S. patent applicationSer. No. 15/616,586 filed Jun. 7, 2017, which is hereby incorporated.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention generally relates to electronic control systems,and more particularly to a method of controlling robotic machines.

Description of the Related Art

Humanoid and tele-operated robots are playing an increasingly importantrole in domestic, manufacturing and medical services. While robots cangreatly simplify human endeavors and offer beneficial outcomes, theypresent an opportunity for malicious actors (hackers) to wreak havoc onthese systems. Such tampering can result in serious bodily injury,financial damage, and less tangible harm such as privacy invasions. Thepossibility of unpleasant results from these intrusions is growing asrobots increasingly display characteristics such as autonomy,decentralized control, collective emergent behavior, and local sensingand communication capabilities. In many cases these robots will serve inextreme conditions, where they have to operate in low-power and harshterrain with potentially unstable connectivity.

The open and uncontrollable nature of the communication medium opensthese systems to a variety of possible cyber security vulnerabilities.There may be a direct attack on the robot, e.g., via wirelesscommunications, or an indirect attack on the computer systemscontrolling these robots, compromising robot operation. In an industrialsetting, a hack meant to simply disrupt a system could end up affectingthe quality of an entire line of products (e.g., automobiles with faultyconstruction) or halting a manufacturing run completely, costingmillions of dollars in productivity. In a personal setting, a hackedservice robot could injure a family member, dispense the wrongmedication in an elder care facility, or provide a hacker with adetailed map of your home.

Threats can be in the form of intentional manipulation attacks whichoccur when an attacker modifies feedback messages (e.g., video feed,haptic feedback), originating from a robot, which may be harder todetect and prevent. In other kinds of attacks, a malicious entity maycause the robot to completely ignore the instructions of a legitimateuser, and to instead perform some other, potentially harmful actionsthat can be either very discreet or very noticeable. In certain cases, anetwork observer may eavesdrop on information exchange between the userand a robot, and based on the collected information, start insertingpoison messages into the network, while still allowing both the benignparties to communicate directly. In message dropping attacks, anintermediary may delay or drop some or all of the received messages,possibly both to and from the human operators.

Solutions have been offered to address cyber security for roboticsystems. These solutions include blanket and/or static means forsecurity by way of pre-defined rules, blacklisting, in-flight dataencryption or other masking/encoding techniques.

SUMMARY OF THE INVENTION

The present invention in at least one embodiment is generally directedto a method of countering security threats for kinematic actions inrobots by receiving input instructions for a kinematic robot, conductingan analysis of the input instructions using a cognitive system whichmaps proposed instructions to either legitimate behavioral actions orillegitimate behavioral actions based on contextual variables relatingto a context of the input instructions, computing a security anomalyindex score based on the analysis wherein the security anomaly indexscore represents a variance of a likely kinematic action of the robot inresponse to the input instructions compared to acceptable actions,determining that the security anomaly index score exceeds apredetermined threshold, by executing fourth instructions in thecomputer system, and responsively generating a security alert for anadministrator of the kinematic robot. The contextual variables caninclude a user profile, a user location, and subject matter of thekinematic actions. The analysis can include comparing one or more inputphrases from the input instructions to predefined classificationmetadata, as well as comparing one or more verbal phrases or bodygestures to corresponding baselines. Different numeric weights can beapplied to the contextual variables, and the computing begins with adefault value for the security anomaly index score and thereafterincrements or decrements the security anomaly index score based on theweights. Additional input instructions for the kinematic robot can bereceived, repeating the analysis and computation while using differentvalues for the weights. The weights can be adjusted based on asupervisory appraisal of the computed security anomaly index score.

The above as well as additional objectives, features, and advantages inthe various embodiments of the present invention will become apparent inthe following detailed written description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages of its various embodiments madeapparent to those skilled in the art by referencing the accompanyingdrawings.

FIG. 1 is a block diagram of a computer system programmed to carry outthe detection and countering of security threats for kinematic actionsin robots in accordance with one implementation of the presentinvention;

FIG. 2 is a block diagram of a cognitive security control system for akinematic robot in accordance with one implementation of the presentinvention;

FIG. 3 is a pictorial representation of an object model for buildinginstruction-action linkages to help identify possible security threatsin accordance with one implementation of the present invention;

FIG. 4 is a chart illustrating the logical flow for a robotic securitycontrol process in accordance with one implementation of the presentinvention; and

FIG. 5 is a table providing an example of received robotic instructionswhich affect a security anomaly index (SAI) score and resulting actionsin accordance with one implementation of the present invention.

The use of the same reference symbols in different drawings indicatessimilar or identical items.

DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

Robotic systems are becoming increasingly pervasive in all facets ofhuman existence, drawing the attention of hackers who may want to impairsuch systems whether for financial gain or out of pure spite. Whilesolutions have been offered to combat cyber security threats to roboticsystems, these solutions often fall short and do not prevent seriousintrusions. In particular, they fail to provide any mechanism toaccurately distinguish between legitimate instructions and potentiallydisruptive ones.

It would, therefore, be desirable to devise an improved method ofcountering security threats which could allow a robotic system to makethreat assessments regarding proposed instructions to the system. Itwould be further advantageous if the method could provide the abilityfor a robotic system to autonomously learn to secure its controlling anddistributed systems, for identification and mitigation of evolving anddynamic security threats. These challenges are overcome in variousembodiments of the present invention by using machine learningtechniques to make a threat assessment regarding input instructions to arobot, in verbal as well as audio/video format, using deep learningalgorithms to recognize and identify anomalies in thespeech/text/content mapped to robotic actions. In an illustrativeimplementation, a cognitive security control system for a kinematicrobot builds an evolving repository of instruction-action linkagesmapping legitimate and illegitimate behavioral actions, in the contextof surrounding variables such as user/organization type and situation(such as tone, mood etc.), geographic or cultural location, subject andpatterns in the conversation, and so on. The cognitive security controlsystem can use a continuous feedback loop by which it observesverbal/written instructions from users/machines and identifies the levelof variance in terms of observed actions/outcomes, to determine asecurity anomaly index (SAI) score using cognitive analysis, based oninput from security experts as well as industry/domain/organizationalinput, best practices, known risks, etc. In one embodiment the presentinvention achieves these objectives by processing input text/audio/videosegments along with other input criteria (such as location, time,conversation topic, tone/mood, user category, etc.), in either asupervised training mode or a run-time mode. In supervised mode, thesystem can perform a supervised review against expected outputs (of theanomaly index), and adjust weights of each input criteria to derive anoutput as close as possible to the expected output. The algorithm canevolve the classification rules for identifying anomalies by analyzing atraining set and applying the rules to a test verification set. Inrun-time mode, the system can iteratively assess verbiage/words foranomalies as a conversation progresses, and determine the anomaly indexusing the classification rules developed based on training andhistorical data. An alert is generated in case the anomaly index risesabove a pre-defined threshold.

With reference now to the figures, and in particular with reference toFIG. 1, there is depicted one embodiment 10 of a computer system inwhich the present invention may be implemented to carry out cognitivelearning to counter security threats in kinematic robots. Computersystem 10 is a symmetric multiprocessor (SMP) system having a pluralityof processors 12 a, 12 b connected to a system bus 14. System bus 14 isfurther connected to and communicates with a combined memorycontroller/host bridge (MC/HB) 16 which provides an interface to systemmemory 18. System memory 18 may be a local memory device oralternatively may include a plurality of distributed memory devices,preferably dynamic random-access memory (DRAM). There may be additionalstructures in the memory hierarchy which are not depicted, such ason-board (L1) and second-level (L2) or third-level (L3) caches. Systemmemory 18 has loaded therein a cognitive analysis (deep learning)application in accordance with the present invention, and may furtherinclude a security controller for the robotic system. MC/HB 16 also hasan interface to peripheral component interconnect (PCI) Express links 20a, 20 b, 20 c. Each PCI Express (PCIe) link 20 a, 20 b is connected to arespective PCIe adaptor 22 a, 22 b, and each PCIe adaptor 22 a, 22 b isconnected to a respective input/output (I/O) device 24 a, 24 b. MC/HB 16may additionally have an interface to an I/O bus 26 which is connectedto a switch (I/O fabric) 28. Switch 28 provides a fan-out for the I/Obus to a plurality of PCI links 20 d, 20 e, 20 f These PCI links areconnected to more PCIe adaptors 22 c, 22 d, 22 e which in turn supportmore I/O devices 24 c, 24 d, 24 e. The I/O devices may include, withoutlimitation, a keyboard, a graphical pointing device (mouse), amicrophone, a display device, speakers, a permanent storage device (harddisk drive) or an array of such storage devices, an optical disk drivewhich receives an optical disk 25 (one example of a computer readablestorage medium) such as a CD or DVD, and a network card. Each PCIeadaptor provides an interface between the PCI link and the respectiveI/O device. MC/HB 16 provides a low latency path through whichprocessors 12 a, 12 b may access PCI devices mapped anywhere within busmemory or I/O address spaces. MC/HB 16 further provides a high bandwidthpath to allow the PCI devices to access memory 18. Switch 28 may providepeer-to-peer communications between different endpoints and this datatraffic does not need to be forwarded to MC/HB 16 if it does not involvecache-coherent memory transfers. Switch 28 is shown as a separatelogical component but it could be integrated into MC/HB 16.

In this embodiment, PCI link 20 c connects MC/HB 16 to a serviceprocessor interface 30 to allow communications between I/O device 24 aand a service processor 32. Service processor 32 is connected toprocessors 12 a, 12 b via a JTAG interface 34, and uses an attentionline 36 which interrupts the operation of processors 12 a, 12 b. Serviceprocessor 32 may have its own local memory 38, and is connected toread-only memory (ROM) 40 which stores various program instructions forsystem startup. Service processor 32 may also have access to a hardwareoperator panel 42 to provide system status and diagnostic information.

In alternative embodiments computer system 10 may include modificationsof these hardware components or their interconnections, or additionalcomponents, so the depicted example should not be construed as implyingany architectural limitations with respect to the present invention. Theinvention may further be implemented in an equivalent cloud computingnetwork.

When computer system 10 is initially powered up, service processor 32uses JTAG interface 34 to interrogate the system (host) processors 12 a,12 b and MC/HB 16. After completing the interrogation, service processor32 acquires an inventory and topology for computer system 10. Serviceprocessor 32 then executes various tests such as built-in-self-tests(BISTs), basic assurance tests (BATs), and memory tests on thecomponents of computer system 10. Any error information for failuresdetected during the testing is reported by service processor 32 tooperator panel 42. If a valid configuration of system resources is stillpossible after taking out any components found to be faulty during thetesting then computer system 10 is allowed to proceed. Executable codeis loaded into memory 18 and service processor 32 releases hostprocessors 12 a, 12 b for execution of the program code, e.g., anoperating system (OS) which is used to launch applications and inparticular the cognitive control system of the present invention,results of which may be stored in a hard disk drive of the system (anI/O device 24). While host processors 12 a, 12 b are executing programcode, service processor 32 may enter a mode of monitoring and reportingany operating parameters or errors, such as the cooling fan speed andoperation, thermal sensors, power supply regulators, and recoverable andnon-recoverable errors reported by any of processors 12 a, 12 b, memory18, and MC/HB 16. Service processor 32 may take further action based onthe type of errors or defined thresholds.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field- programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Computer system 10 carries out program instructions for counteringsecurity threats in kinematic robots that use novel cognitive analysistechniques to detect instruction anomalies. Accordingly, a programembodying the invention may additionally include conventional aspects ofvarious cognitive analysis tools, and these details will become apparentto those skilled in the art upon reference to this disclosure. Acognitive system (sometimes referred to as a deep learning, deepthought, or deep question answering system) is a form of artificialintelligence that uses machine learning and problem solving. Cognitivesystems often employ neural networks although alternative designs exist.The neural network may be of various types. A feedforward neural networkis an artificial neural network wherein connections between the units donot form a cycle. The feedforward neural network was the first andsimplest type of artificial neural network devised. In this network, theinformation moves in only one direction, forward, from the input nodes,through the hidden nodes (if any) and to the output nodes. There are nocycles or loops in the network. As such, it is different from recurrentneural networks. A recurrent neural network is a class of artificialneural network where connections between units form a directed cycle.This creates an internal state of the network which allows it to exhibitdynamic temporal behavior. Unlike feedforward neural networks, recurrentneural networks can use their internal memory to process arbitrarysequences of inputs. A convolution neural network is a specific type offeed-forward neural network based on animal visual perception, and so isparticularly useful in processing image data. Convolutional neuralnetworks are similar to ordinary neural networks but are made up ofneurons that have learnable weights and biases.

A modern implementation of artificial intelligence is the IBM Watson™cognitive technology, which applies advanced natural languageprocessing, information retrieval, knowledge representation, automatedreasoning, and machine learning technologies to the field of open domainquestion answering. Such cognitive systems can rely on existingdocuments (corpora) and analyze them in various ways in order to extractanswers relevant to a query, such as person, location, organization, andparticular objects, or identify positive and negative sentiment.Different techniques can be used to analyze natural language, identifysources, find and generate hypotheses, find and score evidence, andmerge and rank hypotheses. Models for scoring and ranking the answer canbe trained on the basis of large sets of question (input) and answer(output) pairs. The more algorithms that find the same answerindependently, the more likely that answer is correct, resulting in anoverall score or confidence level.

Referring now to FIG. 2, there is depicted a block diagram for acognitive security control system 50 for a kinematic robot in accordancewith one implementation of the present invention. Cognitive securityassessment agent 56 may run on any computer system, such as computersystem 10. According to this implementation, instructions for akinematic robot 70 originate either from a machine via a machinecontroller 52 or from a human user via a user interface 54. Instructionsfrom machine controller 52 may be made by way of programming, using aseparate computer or control system. Instructions from user interface 54may be derived from various forms of user input including text, voice,or other content, e.g., visual. Machine controller 52 and user interface54 may be conventional. The instructions are monitored by a cognitivesecurity assessment agent 56, i.e., intercepted before they are passedonto the kinematic robot 70.

Cognitive security assessment agent 56 uses deep learning technology toanalyze each of the input instructions from machine controller 52 oruser interface 54. This technology may include a classifier. Forexample, natural language classifiers are commonly used in naturallanguage processing systems to identify the type of discourse inconnected text, e.g., a yes/no question, a content question, astatement, an assertion, etc. This service enables developers without abackground in machine learning or statistical algorithms to createnatural language interfaces for their applications. A classifier caninterpret the intent behind commands and returns a correspondingclassification with associated confidence levels. The return value canthen be used to trigger a corresponding action. For example, the IBMWatson™ natural language classifier service applies deep learningtechniques to make predictions about the best predefined classes forshort sentences or phrases. Cognitive security assessment agent 56 alsopreferably includes a self-learning modeler which allows the accuracy ofthe analysis to improve over time using periodic training and feedbackfrom security experts who can provide feedback on the quality ofresults. Scoring and classification of suspicious instructions innatural language text/audio/video communications can be based onhistorical and contextual information, with the potential for highervariance in resultant action/behavior. These features of cognitivesecurity assessment agent 56 can utilize various predefined dataincluding contextual variables 58 such as profiles, locations, andsubjects, classification metadata 60 including operational data,business process patterns, and organization dictionaries, safetytemplates 62 providing kinematic, verbal and gesture baselines, andknowledge corpora 64 which define instruction-action associationsrelating to physical security, cultural attributes, etc., to recognizeand identify variation in patterns of speech/text/content. Variousfeatures of the invention including the cognitive security assessmentagent could be incorporated into the robot itself.

Cognitive security assessment agent 56 carries out a cognitive analysison the input phrases to generate a security anomaly index (SAI) scorewhich is used to make a threat assessment. In one implementation, if theSAI score is higher than a predetermined threshold then the inputinstructions are deemed suspicious/threatening, and cognitive securityassessment agent 56 will suspend execution of the instructions and sendan alert, e.g., to a console 66 for an administrator of the robot. Onlywhen the instructions are deemed “safe”, are they then forwarded to aninstruction-based action controller 68 which then applies them to robot70.

One solution proposed by the present invention can build and leverageinstruction-action linkages based on the robot's previous interactionsto derive extended relationships and aggregated insights, usingassociations of legitimate behavioral actions with a combination ofvarious hierarchical information. In a preferred implementation, theinformation may include (i) a hierarchy of profiles(demographic/organizational) matching different users' persona types,(ii) a hierarchy or map of locations (country, region, province, city,suburb, etc.), (iii) a hierarchy of commands from a dictionary oftopic/subject area, and (iv) a hierarchy of kinematic actions. Thisimplementation is reflected in FIG. 3 which illustrates an object model80 for building instruction-action linkages. According to this example,the input instructions emanate from an individual named John Doe who isresiding in the Tuscany region of Italy. The command relates to foodprocessing (culinary), in this case, cutting, and involves action by theupper arms of the kinematic robot. These and other attributes of theinput instruction are combined to create a security-based kinematicaction linkage (SKAL).

The SKAL graph thereby produced can be compared to other SKAL graphsfrom both legitimate and illegitimate instructions to iteratively anddynamically assess input instructions and vary the confidence score.Different numeric weights can be applied to the contextual variables,and the confidence score can be based on changing weights as aconversation progresses. The apparatus can thus leverage priorconversational context in determining subsequent matches. These deeplearning techniques can be used to categorize and rank specificinstructions (phrases/gestures/body language) based on higher semanticand functional association to a particular subject area orindustry-domain practices. The algorithm can evolve the classificationrules for identifying anomalies by analyzing a training set and applyingthem to a test verification set.

The present invention may be further understood with reference to thechart of

FIG. 4 which depicts the logical flow for a robotic security controlprocess 90 carried out by cognitive agent 56 in accordance with oneimplementation. Process 90 begins with a capture of input instructionsfrom a machine or user (92). The process can initialize the SAI score ata default value. Various instruction-action linkages are then examined.First, the agent can look up instruction-action linkages with profilematching based on the user's persona type (94). If a profile matchexists (96), then the SAI score is decreased in direct proportion to thesum of the weights for the matching criteria, i.e., the score is loweredto reflect increased confidence in the safety of the instructions. If noprofile match exists, the SAI score can be increased in inverseproportion to the sum of weights of matches of the closest similarprofile (100), i.e., raising the score to reflect concern that theinstructions are suspicious. A new instruction-action linkage can thenbe added to the user's profile type, with a higher weight for closerprofile matching (102). Agent 56 can then traverse the location mapassociated with the input instructions to see if there is a matchinglocation (104). If a location match exists (106), the SAI score is againdecreased (108), but if not, the SAI score is again increased (110). Anew instruction-action linkage is added to the locations, again with ahigher weight for closer location matching (112). Agent 56 can then thecommand dictionary associated with robot to see if the inputinstructions match a topic (114). If a subject match exists (116), theSAI score is again decreased (118), but if not, the SAI score is againincreased (120), and another new instruction-action linkage is added forthis topic, with a higher weight for closer subject matching (112). Theresulting SAI score is then compared to a threshold score for this robotcategory (124). If the score is greater than the threshold, an alert issent to the robot administrator along with the SAI score and possiblyother related information including the suspect input instruction (126).If the score is less than the threshold, the instructions are passed onto the robot for execution (128).

A system in accordance with the present invention may thus findapplicability in a wide variety of circumstances to passively observespeech/text/video input to identify instructions resulting in kinematicactions that have higher security anomaly index scores than suitablethresholds, based on machine learning models to observe, score anddetermine suspicious content based on historical datasets, includinglabeled attack data, and situational parameters; for example, in thecase of a robotic surgery, a much more stringent anomaly threshold maybe applied to instructions that seem out of normal sequence. Thecognitive agent can provide the ability to interactively and iterativelyassess security anomalies based on phrases/gestures/body language/voiceintonation by dynamically varying the score based on change in weightsas the interaction progresses; for example, a robotic receptionist mayleverage prior conversational context about a visitor's background indetermining that a subsequent instruction asking to lookup personaldetails of a co-visitor is suspicious. Differential security may beprovided based on a range of inputs such as security experts who canprovide feedback on quality of resultant kinematic behavior, as well asdomain/industry practices and case history, by using deep learningtechniques to categorize sensitive behavior/actions based on highersemantic and functional alignment to that domain than other caseartifacts; for example, to rate an instruction to a service robot in arestaurant to heat a beverage above human-drinkable threshold assuspicious.

An example of how the SAI score might be generated as a conversation ofinstructions progresses in seen in FIG. 5. This example is for a nurserobot performing a preliminary screening of an individual who may havean injured hand, but the instruction sequence has been tampered with inan attempt to carry out intentional harm. When the screening begins, thecognitive agent has set the score to a default value of 50. The firstinstruction is to move the arm of the robot to a handshake position, toprepare for inspection of the patient's hand. This instruction is deemednormal/safe by the cognitive system, so the score is decreased to 45,and the agent passed the instruction to the robot which moves its armaccordingly. The second instruction is to have the robot issue thestatement “Hello, please give me your hand.” This instruction is alsodeemed normal/safe so the score is again decreased, this time to 40, andthe instruction is passed to the robot which uses a text-to-speechfunction to audibly deliver the statement. The third instruction is tosense the patient's hand, which is considered normal/safe but is notdeemed significant enough by the cognitive agent to change the score,which remains at 40, so the instruction passes and the robot waits forsensory input indicating that the patient has placed his hand at therobotic arm. The next instruction is to have the robot issue thestatement “This is how you sing” which is considered anomalous based onthe cognitive analysis. The result is an increase in the SAI score to55; however, for this example the threshold is set at 75 so the robot isallowed to proceed with this speech delivery. Finally, an instruction isgiven to squeeze the robotic hand until the pneumatic pressure is acertain value. This value is deemed to be dangerous by the cognitiveagent which raises the score, this time to 80, which exceeds thethreshold. The resulting action is to interrupt control of the robot andsend an alarm.

A cognitive agent constructed in accordance with the present inventionmay thus be easily inserted within existing frameworks for controllingactions of kinematic robots. The apparatus can be used in traditionalsettings as well as in IaaS (Infrastructure as a Service) and PaaS(Platform as a Service) based offerings. The apparatus can also beleveraged in Cognitive, Big Data and Analytics solutions to providebusiness and commercial value.

Although the invention has been described with reference to specificembodiments, this description is not meant to be construed in a limitingsense. Various modifications of the disclosed embodiments, as well asalternative embodiments of the invention, will become apparent topersons skilled in the art upon reference to the description of theinvention. It is therefore contemplated that such modifications can bemade without departing from the spirit or scope of the present inventionas defined in the appended claims.

1. A method of countering security threats for kinematic actions inrobots comprising: receiving input instructions for a kinematic robot,by executing first instructions in a computer system; conducting ananalysis of the input instructions using a cognitive system which mapsproposed instructions to either legitimate behavioral actions orillegitimate behavioral actions based on contextual variables relatingto a context of the input instructions, by executing second instructionsin the computer system; computing a security anomaly index score basedon the analysis wherein the security anomaly index score represents avariance of a likely kinematic action of the robot in response to theinput instructions compared to acceptable actions, by executing thirdinstructions in the computer system; determining that the securityanomaly index score exceeds a predetermined threshold, by executingfourth instructions in the computer system; and responsive to saiddetermining, generating a security alert for an administrator of thekinematic robot, by executing fifth instructions in the computer system.2. The method of claim 1 wherein the contextual variables include a userprofile, a user location, and subject matter of the kinematic actions.3. The method of claim 1 wherein said conducting of the analysisincludes comparing one or more input phrases from the input instructionsto predefined classification metadata.
 4. The method of claim 1 whereinsaid conducting of the analysis includes comparing one or more verbalphrases or body gestures to corresponding baselines.
 5. The method ofclaim 1 wherein different numeric weights are applied to the contextualvariables, and said computing begins with a default value for thesecurity anomaly index score and thereafter increments or decrements thesecurity anomaly index score based on the weights.
 6. The method ofclaim 5 wherein the input instructions are first input instructions, andfurther comprising receiving second input instructions for the kinematicrobot, and repeating said conducting and said computing for the secondinput instructions while using different values for the weights.
 7. Themethod of claim 5 further comprising receiving a supervisory appraisalof the computed security anomaly index score, and adjusting the weightsbased on the supervisory appraisal. 8.-20. (canceled)